Overview

Dataset statistics

Number of variables23
Number of observations2938
Missing cells2563
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory528.0 KiB
Average record size in memory184.0 B

Variable types

NUM20
CAT3

Warnings

Country has a high cardinality: 193 distinct values High cardinality
under-five_deaths is highly correlated with infant_deathsHigh correlation
infant_deaths is highly correlated with under-five_deathsHigh correlation
thinness_5-9_years is highly correlated with thinness_1-19_yearsHigh correlation
thinness_1-19_years is highly correlated with thinness_5-9_yearsHigh correlation
Alcohol has 194 (6.6%) missing values Missing
Hepatitis_B has 553 (18.8%) missing values Missing
BMI has 34 (1.2%) missing values Missing
Total_expenditure has 226 (7.7%) missing values Missing
GDP has 448 (15.2%) missing values Missing
Population has 652 (22.2%) missing values Missing
thinness_1-19_years has 34 (1.2%) missing values Missing
thinness_5-9_years has 34 (1.2%) missing values Missing
Income_composition_of_resources has 167 (5.7%) missing values Missing
Schooling has 163 (5.5%) missing values Missing
infant_deaths has 848 (28.9%) zeros Zeros
percentage_expenditure has 611 (20.8%) zeros Zeros
Measles has 983 (33.5%) zeros Zeros
under-five_deaths has 785 (26.7%) zeros Zeros
Income_composition_of_resources has 130 (4.4%) zeros Zeros

Reproduction

Analysis started2020-11-01 06:45:16.237947
Analysis finished2020-11-01 06:48:44.022808
Duration3 minutes and 27.78 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Country
Categorical

HIGH CARDINALITY

Distinct193
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
Jordan
 
16
Myanmar
 
16
Dominican Republic
 
16
Republic of Korea
 
16
Egypt
 
16
Other values (188)
2858 
ValueCountFrequency (%) 
Jordan160.5%
 
Myanmar160.5%
 
Dominican Republic160.5%
 
Republic of Korea160.5%
 
Egypt160.5%
 
Slovenia160.5%
 
Mozambique160.5%
 
Saint Vincent and the Grenadines160.5%
 
Georgia160.5%
 
Bahrain160.5%
 
Other values (183)277894.6%
 
2020-11-01T12:18:44.623938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique10 ?
Unique (%)0.3%
2020-11-01T12:18:44.908763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length7
Mean length10.0357386
Min length4

Year
Real number (ℝ≥0)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.51872
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:45.149612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.61384094
Coefficient of variation (CV)0.002298280406
Kurtosis-1.213721712
Mean2007.51872
Median Absolute Deviation (MAD)4
Skewness-0.006409027359
Sum5898090
Variance21.28752822
MonotocityNot monotonic
2020-11-01T12:18:45.381469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
20131936.6%
 
20151836.2%
 
20111836.2%
 
20091836.2%
 
20071836.2%
 
20051836.2%
 
20031836.2%
 
20011836.2%
 
20141836.2%
 
20121836.2%
 
Other values (6)109837.4%
 
ValueCountFrequency (%) 
20001836.2%
 
20011836.2%
 
20021836.2%
 
20031836.2%
 
20041836.2%
 
ValueCountFrequency (%) 
20151836.2%
 
20141836.2%
 
20131936.6%
 
20121836.2%
 
20111836.2%
 

Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
Developing
2426 
Developed
512 
ValueCountFrequency (%) 
Developing242682.6%
 
Developed51217.4%
 
2020-11-01T12:18:45.636363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-01T12:18:45.804258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:46.192017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.82573179
Min length9

Life_expectancy
Real number (ℝ≥0)

Distinct362
Distinct (%)12.4%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.22493169
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:46.484837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.4
Q163.1
median72.1
Q375.7
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation9.523867488
Coefficient of variation (CV)0.1375785754
Kurtosis-0.2344773942
Mean69.22493169
Median Absolute Deviation (MAD)5.8
Skewness-0.6386047359
Sum202690.6
Variance90.70405193
MonotocityNot monotonic
2020-11-01T12:18:46.759666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
73451.5%
 
75331.1%
 
78311.1%
 
73.6281.0%
 
73.9250.9%
 
81250.9%
 
76250.9%
 
74.7240.8%
 
74.5240.8%
 
74.1230.8%
 
Other values (352)264590.0%
 
ValueCountFrequency (%) 
36.31< 0.1%
 
391< 0.1%
 
411< 0.1%
 
41.51< 0.1%
 
42.31< 0.1%
 
ValueCountFrequency (%) 
89110.4%
 
88100.3%
 
8790.3%
 
86150.5%
 
85120.4%
 

Adult_Mortality
Real number (ℝ≥0)

Distinct425
Distinct (%)14.5%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean164.7964481
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:47.060601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile398.3
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.292079
Coefficient of variation (CV)0.754215764
Kurtosis1.748860208
Mean164.7964481
Median Absolute Deviation (MAD)76
Skewness1.174369488
Sum482524
Variance15448.5209
MonotocityNot monotonic
2020-11-01T12:18:47.338429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12341.2%
 
14301.0%
 
16291.0%
 
11250.9%
 
138250.9%
 
19230.8%
 
144220.7%
 
17210.7%
 
15210.7%
 
13210.7%
 
Other values (415)267791.1%
 
ValueCountFrequency (%) 
1120.4%
 
280.3%
 
360.2%
 
440.1%
 
520.1%
 
ValueCountFrequency (%) 
7231< 0.1%
 
7171< 0.1%
 
7151< 0.1%
 
6991< 0.1%
 
6931< 0.1%
 

infant_deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.30394826
Minimum0
Maximum1800
Zeros848
Zeros (%)28.9%
Memory size23.0 KiB
2020-11-01T12:18:47.618446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.15
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation117.9265013
Coefficient of variation (CV)3.891456661
Kurtosis116.0427561
Mean30.30394826
Median Absolute Deviation (MAD)3
Skewness9.78696295
Sum89033
Variance13906.65971
MonotocityNot monotonic
2020-11-01T12:18:47.885281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
084828.9%
 
134211.6%
 
22036.9%
 
31756.0%
 
4963.3%
 
8571.9%
 
7531.8%
 
10481.6%
 
9481.6%
 
6461.6%
 
Other values (199)102234.8%
 
ValueCountFrequency (%) 
084828.9%
 
134211.6%
 
22036.9%
 
31756.0%
 
4963.3%
 
ValueCountFrequency (%) 
180020.1%
 
170020.1%
 
16001< 0.1%
 
150020.1%
 
14001< 0.1%
 

Alcohol
Real number (ℝ≥0)

MISSING

Distinct1076
Distinct (%)39.2%
Missing194
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean4.602860787
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:48.308515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.8775
median3.755
Q37.7025
95-th percentile11.96
Maximum17.87
Range17.86
Interquartile range (IQR)6.825

Descriptive statistics

Standard deviation4.052412659
Coefficient of variation (CV)0.8804117366
Kurtosis-0.8029092244
Mean4.602860787
Median Absolute Deviation (MAD)3.245
Skewness0.5895625281
Sum12630.25
Variance16.42204836
MonotocityNot monotonic
2020-11-01T12:18:48.561360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.012889.8%
 
0.03150.5%
 
0.04130.4%
 
0.02120.4%
 
0.09120.4%
 
1.18100.3%
 
0.21100.3%
 
0.06100.3%
 
0.0890.3%
 
0.4990.3%
 
Other values (1066)235680.2%
 
(Missing)1946.6%
 
ValueCountFrequency (%) 
0.012889.8%
 
0.02120.4%
 
0.03150.5%
 
0.04130.4%
 
0.0590.3%
 
ValueCountFrequency (%) 
17.871< 0.1%
 
17.311< 0.1%
 
16.991< 0.1%
 
16.581< 0.1%
 
16.351< 0.1%
 

percentage_expenditure
Real number (ℝ≥0)

ZEROS

Distinct2328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.2512955
Minimum0
Maximum19479.91161
Zeros611
Zeros (%)20.8%
Memory size23.0 KiB
2020-11-01T12:18:48.864244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.685342585
median64.91290604
Q3441.5341444
95-th percentile4506.638496
Maximum19479.91161
Range19479.91161
Interquartile range (IQR)436.8488018

Descriptive statistics

Standard deviation1987.914858
Coefficient of variation (CV)2.692734669
Kurtosis26.57338739
Mean738.2512955
Median Absolute Deviation (MAD)64.91290604
Skewness4.652051348
Sum2168982.306
Variance3951805.483
MonotocityNot monotonic
2020-11-01T12:18:49.137075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
061120.8%
 
345.90442581< 0.1%
 
2698.018171< 0.1%
 
3.433343641< 0.1%
 
8.7582145381< 0.1%
 
5.1032494381< 0.1%
 
70.271131791< 0.1%
 
6164.4554021< 0.1%
 
0.9624970521< 0.1%
 
253.40223381< 0.1%
 
Other values (2318)231878.9%
 
ValueCountFrequency (%) 
061120.8%
 
0.099872191< 0.1%
 
0.1080559731< 0.1%
 
0.275648261< 0.1%
 
0.3284180561< 0.1%
 
ValueCountFrequency (%) 
19479.911611< 0.1%
 
19099.045061< 0.1%
 
18961.34861< 0.1%
 
18822.867321< 0.1%
 
18379.329741< 0.1%
 

Hepatitis_B
Real number (ℝ≥0)

MISSING

Distinct87
Distinct (%)3.6%
Missing553
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean80.94046122
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:49.420902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.07001559
Coefficient of variation (CV)0.3097340343
Kurtosis2.770259399
Mean80.94046122
Median Absolute Deviation (MAD)6
Skewness-1.930845104
Sum193043
Variance628.5056818
MonotocityNot monotonic
2020-11-01T12:18:49.706723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
992408.2%
 
982107.1%
 
961675.7%
 
971555.3%
 
951495.1%
 
941274.3%
 
931013.4%
 
92923.1%
 
91752.6%
 
89712.4%
 
Other values (77)99834.0%
 
(Missing)55318.8%
 
ValueCountFrequency (%) 
11< 0.1%
 
240.1%
 
440.1%
 
590.3%
 
6170.6%
 
ValueCountFrequency (%) 
992408.2%
 
982107.1%
 
971555.3%
 
961675.7%
 
951495.1%
 

Measles
Real number (ℝ≥0)

ZEROS

Distinct958
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.59224
Minimum0
Maximum212183
Zeros983
Zeros (%)33.5%
Memory size23.0 KiB
2020-11-01T12:18:50.009803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3360.25
95-th percentile9985.55
Maximum212183
Range212183
Interquartile range (IQR)360.25

Descriptive statistics

Standard deviation11467.27249
Coefficient of variation (CV)4.739340911
Kurtosis114.8599032
Mean2419.59224
Median Absolute Deviation (MAD)17
Skewness9.441331947
Sum7108762
Variance131498338.3
MonotocityNot monotonic
2020-11-01T12:18:50.297624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
098333.5%
 
11043.5%
 
2682.3%
 
3441.5%
 
4331.1%
 
6291.0%
 
7281.0%
 
5250.9%
 
8240.8%
 
9220.7%
 
Other values (948)157853.7%
 
ValueCountFrequency (%) 
098333.5%
 
11043.5%
 
2682.3%
 
3441.5%
 
4331.1%
 
ValueCountFrequency (%) 
2121831< 0.1%
 
1824851< 0.1%
 
1681071< 0.1%
 
1412581< 0.1%
 
1338021< 0.1%
 

BMI
Real number (ℝ≥0)

MISSING

Distinct608
Distinct (%)20.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.32124656
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:50.563458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q119.3
median43.5
Q356.2
95-th percentile64.785
Maximum87.3
Range86.3
Interquartile range (IQR)36.9

Descriptive statistics

Standard deviation20.0440335
Coefficient of variation (CV)0.5230527528
Kurtosis-1.291095468
Mean38.32124656
Median Absolute Deviation (MAD)16.3
Skewness-0.2193116034
Sum111284.9
Variance401.7632791
MonotocityNot monotonic
2020-11-01T12:18:50.845284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
58.5180.6%
 
57160.5%
 
55.8160.5%
 
54.2150.5%
 
59.9150.5%
 
59.3140.5%
 
55130.4%
 
56.5130.4%
 
59.4130.4%
 
52.8130.4%
 
Other values (598)275893.9%
 
(Missing)341.2%
 
ValueCountFrequency (%) 
11< 0.1%
 
1.420.1%
 
1.81< 0.1%
 
1.91< 0.1%
 
21< 0.1%
 
ValueCountFrequency (%) 
87.31< 0.1%
 
83.31< 0.1%
 
82.81< 0.1%
 
81.61< 0.1%
 
79.31< 0.1%
 

under-five_deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.0357386
Minimum0
Maximum2500
Zeros785
Zeros (%)26.7%
Memory size23.0 KiB
2020-11-01T12:18:51.174211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.4455484
Coefficient of variation (CV)3.816884246
Kurtosis109.7527951
Mean42.0357386
Median Absolute Deviation (MAD)4
Skewness9.495064657
Sum123501
Variance25742.774
MonotocityNot monotonic
2020-11-01T12:18:51.833804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
078526.7%
 
136112.3%
 
21635.5%
 
41615.5%
 
31294.4%
 
12531.8%
 
8491.7%
 
6481.6%
 
10471.6%
 
5441.5%
 
Other values (242)109837.4%
 
ValueCountFrequency (%) 
078526.7%
 
136112.3%
 
21635.5%
 
31294.4%
 
41615.5%
 
ValueCountFrequency (%) 
25001< 0.1%
 
24001< 0.1%
 
23001< 0.1%
 
22001< 0.1%
 
21001< 0.1%
 

Polio
Real number (ℝ≥0)

Distinct73
Distinct (%)2.5%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.55018842
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:52.156603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.42804595
Coefficient of variation (CV)0.2838036641
Kurtosis3.776509819
Mean82.55018842
Median Absolute Deviation (MAD)6
Skewness-2.098053249
Sum240964
Variance548.873337
MonotocityNot monotonic
2020-11-01T12:18:52.477405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9937612.8%
 
982558.7%
 
962077.0%
 
972057.0%
 
951806.1%
 
941595.4%
 
931204.1%
 
92963.3%
 
91883.0%
 
9712.4%
 
Other values (63)116239.6%
 
ValueCountFrequency (%) 
370.2%
 
4110.4%
 
580.3%
 
6110.4%
 
7240.8%
 
ValueCountFrequency (%) 
9937612.8%
 
982558.7%
 
972057.0%
 
962077.0%
 
951806.1%
 

Total_expenditure
Real number (ℝ≥0)

MISSING

Distinct818
Distinct (%)30.2%
Missing226
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.938189528
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:52.839355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.93
Q14.26
median5.755
Q37.4925
95-th percentile9.76
Maximum17.6
Range17.23
Interquartile range (IQR)3.2325

Descriptive statistics

Standard deviation2.498319672
Coefficient of variation (CV)0.4207207703
Kurtosis1.156270469
Mean5.938189528
Median Absolute Deviation (MAD)1.59
Skewness0.6186855521
Sum16104.37
Variance6.241601184
MonotocityNot monotonic
2020-11-01T12:18:53.096197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.6150.5%
 
6.7120.4%
 
5.6110.4%
 
5.64100.3%
 
3.4100.3%
 
9.1100.3%
 
5.3100.3%
 
5.25100.3%
 
5.9100.3%
 
5.2990.3%
 
Other values (808)260588.7%
 
(Missing)2267.7%
 
ValueCountFrequency (%) 
0.371< 0.1%
 
0.651< 0.1%
 
0.741< 0.1%
 
0.761< 0.1%
 
0.921< 0.1%
 
ValueCountFrequency (%) 
17.61< 0.1%
 
17.241< 0.1%
 
17.220.1%
 
17.141< 0.1%
 
171< 0.1%
 

Diphtheria
Real number (ℝ≥0)

Distinct81
Distinct (%)2.8%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.32408359
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:53.370025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.71691207
Coefficient of variation (CV)0.2880920265
Kurtosis3.558143
Mean82.32408359
Median Absolute Deviation (MAD)6
Skewness-2.072752929
Sum240304
Variance562.4919181
MonotocityNot monotonic
2020-11-01T12:18:53.627868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9935011.9%
 
982548.6%
 
972057.0%
 
962016.8%
 
952006.8%
 
941495.1%
 
931204.1%
 
921003.4%
 
91913.1%
 
89762.6%
 
Other values (71)117339.9%
 
ValueCountFrequency (%) 
21< 0.1%
 
340.1%
 
4120.4%
 
5100.3%
 
6160.5%
 
ValueCountFrequency (%) 
9935011.9%
 
982548.6%
 
972057.0%
 
962016.8%
 
952006.8%
 

HIV/AIDS
Real number (ℝ≥0)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.742103472
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:53.876712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.515
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.077784531
Coefficient of variation (CV)2.914743363
Kurtosis34.89200787
Mean1.742103472
Median Absolute Deviation (MAD)0
Skewness5.396112042
Sum5118.3
Variance25.78389574
MonotocityNot monotonic
2020-11-01T12:18:54.143548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.1178160.6%
 
0.21244.2%
 
0.31153.9%
 
0.4692.3%
 
0.5421.4%
 
0.6351.2%
 
0.8321.1%
 
0.9321.1%
 
0.7291.0%
 
1.5210.7%
 
Other values (190)65822.4%
 
ValueCountFrequency (%) 
0.1178160.6%
 
0.21244.2%
 
0.31153.9%
 
0.4692.3%
 
0.5421.4%
 
ValueCountFrequency (%) 
50.61< 0.1%
 
50.31< 0.1%
 
49.91< 0.1%
 
49.11< 0.1%
 
48.81< 0.1%
 

GDP
Real number (ℝ≥0)

MISSING

Distinct2490
Distinct (%)100.0%
Missing448
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean7483.158469
Minimum1.68135
Maximum119172.7418
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:54.484560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile68.05001537
Q1463.935626
median1766.947595
Q35910.806335
95-th percentile41606.84833
Maximum119172.7418
Range119171.0605
Interquartile range (IQR)5446.870709

Descriptive statistics

Standard deviation14270.16934
Coefficient of variation (CV)1.906971421
Kurtosis12.33307364
Mean7483.158469
Median Absolute Deviation (MAD)1592.456071
Skewness3.20665487
Sum18633064.59
Variance203637733
MonotocityNot monotonic
2020-11-01T12:18:54.771382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1276.2651< 0.1%
 
3638.959461< 0.1%
 
2158.2991< 0.1%
 
1768.921321< 0.1%
 
261.4568821< 0.1%
 
558.2211441< 0.1%
 
38532.4881< 0.1%
 
5.66872641< 0.1%
 
2519.7373871< 0.1%
 
1922.413881< 0.1%
 
Other values (2480)248084.4%
 
(Missing)44815.2%
 
ValueCountFrequency (%) 
1.681351< 0.1%
 
3.6859491< 0.1%
 
4.61357451< 0.1%
 
5.66872641< 0.1%
 
8.3764321< 0.1%
 
ValueCountFrequency (%) 
119172.74181< 0.1%
 
115761.5771< 0.1%
 
114293.84331< 0.1%
 
113751.851< 0.1%
 
89739.71171< 0.1%
 

Population
Real number (ℝ≥0)

MISSING

Distinct2278
Distinct (%)99.7%
Missing652
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean12753375.12
Minimum34
Maximum1293859294
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:55.086215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile9617.5
Q1195793.25
median1386542
Q37420359
95-th percentile47554415.75
Maximum1293859294
Range1293859260
Interquartile range (IQR)7224565.75

Descriptive statistics

Standard deviation61012096.51
Coefficient of variation (CV)4.783996074
Kurtosis298.0102666
Mean12753375.12
Median Absolute Deviation (MAD)1357309.5
Skewness15.9162356
Sum2.915421552e+10
Variance3.72247592e+15
MonotocityNot monotonic
2020-11-01T12:18:55.454985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
44440.1%
 
114120.1%
 
71823920.1%
 
2686820.1%
 
12744520.1%
 
29220.1%
 
9398481< 0.1%
 
391454881< 0.1%
 
1276581< 0.1%
 
132811< 0.1%
 
Other values (2268)226877.2%
 
(Missing)65222.2%
 
ValueCountFrequency (%) 
341< 0.1%
 
361< 0.1%
 
411< 0.1%
 
431< 0.1%
 
1231< 0.1%
 
ValueCountFrequency (%) 
12938592941< 0.1%
 
11796812391< 0.1%
 
11619777191< 0.1%
 
11441186741< 0.1%
 
11261357771< 0.1%
 

thinness_1-19_years
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct200
Distinct (%)6.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.839703857
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:55.755799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.3
Q37.2
95-th percentile13.8
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.420194947
Coefficient of variation (CV)0.9133193018
Kurtosis3.97043867
Mean4.839703857
Median Absolute Deviation (MAD)2.3
Skewness1.711471088
Sum14054.5
Variance19.53812337
MonotocityNot monotonic
2020-11-01T12:18:56.035625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1742.5%
 
1.9652.2%
 
0.8642.2%
 
0.7632.1%
 
1.2622.1%
 
2.1612.1%
 
1.5602.0%
 
2.2582.0%
 
2571.9%
 
0.9571.9%
 
Other values (190)228377.7%
 
ValueCountFrequency (%) 
0.1281.0%
 
0.2401.4%
 
0.3321.1%
 
0.450.2%
 
0.5351.2%
 
ValueCountFrequency (%) 
27.71< 0.1%
 
27.51< 0.1%
 
27.41< 0.1%
 
27.31< 0.1%
 
27.220.1%
 

thinness_5-9_years
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct207
Distinct (%)7.1%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.870316804
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2020-11-01T12:18:56.393404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.5
median3.3
Q37.2
95-th percentile13.8
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.508882087
Coefficient of variation (CV)0.9257882532
Kurtosis4.358730342
Mean4.870316804
Median Absolute Deviation (MAD)2.3
Skewness1.777423977
Sum14143.4
Variance20.33001767
MonotocityNot monotonic
2020-11-01T12:18:56.696216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9692.3%
 
1.1672.3%
 
0.5632.1%
 
1.9632.1%
 
1622.1%
 
2.1612.1%
 
1.3592.0%
 
1.5571.9%
 
1.7551.9%
 
0.6541.8%
 
Other values (197)229478.1%
 
ValueCountFrequency (%) 
0.1371.3%
 
0.2451.5%
 
0.3250.9%
 
0.4170.6%
 
0.5632.1%
 
ValueCountFrequency (%) 
28.61< 0.1%
 
28.51< 0.1%
 
28.41< 0.1%
 
28.31< 0.1%
 
28.21< 0.1%
 

Income_composition_of_resources
Real number (ℝ≥0)

MISSING
ZEROS

Distinct625
Distinct (%)22.6%
Missing167
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.6275510646
Minimum0
Maximum0.948
Zeros130
Zeros (%)4.4%
Memory size23.0 KiB
2020-11-01T12:18:57.304839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.277
Q10.493
median0.677
Q30.779
95-th percentile0.892
Maximum0.948
Range0.948
Interquartile range (IQR)0.286

Descriptive statistics

Standard deviation0.2109035552
Coefficient of variation (CV)0.3360739341
Kurtosis1.392814239
Mean0.6275510646
Median Absolute Deviation (MAD)0.127
Skewness-1.14376272
Sum1738.944
Variance0.04448030958
MonotocityNot monotonic
2020-11-01T12:18:57.738195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01304.4%
 
0.7170.6%
 
0.739130.4%
 
0.636120.4%
 
0.714120.4%
 
0.86110.4%
 
0.703110.4%
 
0.723110.4%
 
0.734110.4%
 
0.877110.4%
 
Other values (615)253286.2%
 
(Missing)1675.7%
 
ValueCountFrequency (%) 
01304.4%
 
0.2531< 0.1%
 
0.2551< 0.1%
 
0.2611< 0.1%
 
0.2661< 0.1%
 
ValueCountFrequency (%) 
0.9481< 0.1%
 
0.9451< 0.1%
 
0.9421< 0.1%
 
0.9411< 0.1%
 
0.9391< 0.1%
 

Schooling
Real number (ℝ≥0)

MISSING

Distinct173
Distinct (%)6.2%
Missing163
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean11.99279279
Minimum0
Maximum20.7
Zeros28
Zeros (%)1.0%
Memory size23.0 KiB
2020-11-01T12:18:58.013323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q110.1
median12.3
Q314.3
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.358919721
Coefficient of variation (CV)0.2800781919
Kurtosis0.8861512689
Mean11.99279279
Median Absolute Deviation (MAD)2.1
Skewness-0.6024365419
Sum33280
Variance11.28234169
MonotocityNot monotonic
2020-11-01T12:18:58.280586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12.9582.0%
 
13.3521.8%
 
12.5491.7%
 
12.8461.6%
 
12.3441.5%
 
12.6431.5%
 
12.4421.4%
 
10.7411.4%
 
11.9411.4%
 
12.7401.4%
 
Other values (163)231978.9%
 
(Missing)1635.5%
 
ValueCountFrequency (%) 
0281.0%
 
2.81< 0.1%
 
2.940.1%
 
31< 0.1%
 
3.11< 0.1%
 
ValueCountFrequency (%) 
20.71< 0.1%
 
20.61< 0.1%
 
20.51< 0.1%
 
20.430.1%
 
20.340.1%
 

Continent
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
Africa
864 
Asia
752 
Europe
626 
Americas
530 
Oceania
166 
ValueCountFrequency (%) 
Africa86429.4%
 
Asia75225.6%
 
Europe62621.3%
 
Americas53018.0%
 
Oceania1665.7%
 
2020-11-01T12:18:58.604387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-01T12:18:58.773283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:59.044165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length6
Mean length5.905377808
Min length4

Interactions

2020-11-01T12:16:23.949647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:25.165167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:25.403021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:25.651867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:25.926695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:26.405402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:26.659242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:26.902094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:27.182919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:27.429765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:27.660624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:27.892479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:28.138327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:28.425149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:28.685987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:28.998793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:29.482846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:29.717214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:29.938509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:30.172876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:30.461631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:30.684726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:30.892528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:31.113937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:31.341529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:31.558393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:31.796245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:32.017108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:32.234976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:32.451840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:32.664709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:32.863585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:33.079452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:33.282325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:33.603127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:33.824988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:34.040857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:34.246730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:34.573525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:34.952291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:35.171158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:35.406013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:35.622876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:35.867726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:36.112572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:36.345431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:36.585280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:36.818136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:37.045997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:37.276852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:37.502712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:37.732571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:37.981417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:38.274233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:38.544067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:38.781922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:39.039047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:39.327867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:39.582713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:39.952482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:40.411198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:40.674036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:40.918885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:41.213701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:41.520513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:41.810365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:42.138197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:42.387167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:42.859872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:43.217651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:43.473492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:43.790296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:44.111098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:16:44.421906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-01T12:17:42.713995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:42.956845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:43.222682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:43.526495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:51.391466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:53.909774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:54.160617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:54.383482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:54.611340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:54.848194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:55.077049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:55.313904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:55.788968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:56.007833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:56.232694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:56.473544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:56.943254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:57.214086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:57.439946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:57.675802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:57.905661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:58.152504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:58.382365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:58.618216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:58.866063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:59.111911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:59.363755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:17:59.596612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:05.899255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:06.133624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:06.380438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:06.617289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:06.861139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:07.110986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:07.349838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:07.915485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:08.381446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:08.663270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:08.913116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:09.154965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:09.392351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:09.646950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:09.943813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:10.182271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:10.415552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:10.650408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:10.906249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:11.143102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:11.394948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:11.620805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:11.833672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:12.049539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:12.270403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:12.479272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:12.704133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:12.928997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:13.156855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:13.593586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:13.848426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:14.067291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:14.296149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:14.520009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:14.731879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:14.955740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:15.187596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:15.419453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:15.643315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:15.868176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:16.098034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:16.325891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:16.533765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:16.752626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:17.004473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:17.247322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:17.481177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:17.709036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:17.934897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:18.177744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:18.405604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:18.626467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:19.103172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:19.381999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:19.604861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:19.854709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:20.102555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:20.332412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:20.568264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:20.785133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:21.054281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:21.288134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:21.502003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:21.742851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:21.984704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:22.202568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:22.411440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:22.658287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:22.916126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:23.175651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:23.400204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:23.612076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:23.829942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:24.083810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:24.512543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:24.799367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:25.033223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:25.279071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:25.524918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:25.737787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:25.961648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:26.203497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:26.432355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:26.659215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:26.879078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:27.121929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:27.345789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:27.592639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:27.825492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:28.053331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:28.293671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:28.525526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-01T12:19:00.388143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-01T12:19:01.526834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-01T12:19:02.099481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-01T12:19:03.053909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-01T12:19:03.564737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-01T12:18:34.772131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:37.917469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:42.102775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-01T12:18:43.364571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

CountryYearStatusLife_expectancyAdult_Mortalityinfant_deathsAlcoholpercentage_expenditureHepatitis_BMeaslesBMIunder-five_deathsPolioTotal_expenditureDiphtheriaHIV/AIDSGDPPopulationthinness_1-19_yearsthinness_5-9_yearsIncome_composition_of_resourcesSchoolingContinent
0Afghanistan2015Developing65.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1Asia
1Afghanistan2014Developing59.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0Asia
2Afghanistan2013Developing59.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9Asia
3Afghanistan2012Developing59.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8Asia
4Afghanistan2011Developing59.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5Asia
5Afghanistan2010Developing58.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2Asia
6Afghanistan2009Developing58.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9Asia
7Afghanistan2008Developing58.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7Asia
8Afghanistan2007Developing57.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4Asia
9Afghanistan2006Developing57.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1Asia

Last rows

CountryYearStatusLife_expectancyAdult_Mortalityinfant_deathsAlcoholpercentage_expenditureHepatitis_BMeaslesBMIunder-five_deathsPolioTotal_expenditureDiphtheriaHIV/AIDSGDPPopulationthinness_1-19_yearsthinness_5-9_yearsIncome_composition_of_resourcesSchoolingContinent
2928Zimbabwe2009Developing50.0587.0304.641.04002173.085329.04569.06.2673.018.165.8241211381599.07.57.40.4199.9Africa
2929Zimbabwe2008Developing48.2632.0303.5620.84342975.0028.64675.04.9675.020.5325.67857313558469.07.87.80.4219.7Africa
2930Zimbabwe2007Developing46.667.0293.8829.81456672.024228.24673.04.4773.023.7396.9982171332999.08.28.20.4149.6Africa
2931Zimbabwe2006Developing45.47.0284.5734.26216968.021227.94571.05.127.026.8414.79623213124267.08.68.60.4089.5Africa
2932Zimbabwe2005Developing44.6717.0284.148.71740965.042027.54369.06.4468.030.3444.765750129432.09.09.00.4069.3Africa
2933Zimbabwe2004Developing44.3723.0274.360.00000068.03127.14267.07.1365.033.6454.36665412777511.09.49.40.4079.2Africa
2934Zimbabwe2003Developing44.5715.0264.060.0000007.099826.7417.06.5268.036.7453.35115512633897.09.89.90.4189.5Africa
2935Zimbabwe2002Developing44.873.0254.430.00000073.030426.34073.06.5371.039.857.348340125525.01.21.30.42710.0Africa
2936Zimbabwe2001Developing45.3686.0251.720.00000076.052925.93976.06.1675.042.1548.58731212366165.01.61.70.4279.8Africa
2937Zimbabwe2000Developing46.0665.0241.680.00000079.0148325.53978.07.1078.043.5547.35887912222251.011.011.20.4349.8Africa